EMQIT: a machine learning approach for energy based PWM matrix quality improvement.

ABSTRACT:

Background

Transcription factor binding affinities to DNA play a key role for the gene regulation. Learning the specificity of the mechanisms of binding TFs to DNA is important both to experimentalists and theoreticians. With the development of high-throughput methods such as, e.g., ChiP-seq the need to provide unbiased models of binding events has been made apparent. We present EMQIT a modification to the approach introduced by Alamanova et al. and later implemented as 3DTF server. We observed that tuning of Boltzmann factor weights, used for conversion of calculated energies to nucleotide probabilities, has a significant impact on the quality of the associated PWM matrix.

Results

Consequently, we proposed to use receiver operator characteristics curves and the 10-fold cros

SUBMITTER: Smolinska K 

PROVIDER: S-EPMC5539975 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature